New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Automatic Differentiation #24
Conversation
The addition of |
Okay here is how I think I handle
This setup should handle the new The downside to this is if users wanted to use the |
Okay I believe to have fixed my NumPy woes. Now I need to create autodiff tests for all the available estimating equations (to be safe everything works with what I provide to users). |
This allows a user to bootstrap if the bread fails to compute
This branch proposes an implementation of automatic differentiation to compute the bread, as mentioned in #23. While there are libraries, like JAX, that implement this, I do not want to add dependencies. Therefore, I have implemented a version of automatic differentiation.
Note that this branch is still under active development. I have not fully tested my implementation of computing the bread. Therefore, only use this branch for preliminary testing.
Note: I am planning on keeping the derivative approximation of the bread as the default. The exact derivatives are going to be an alternative option. Ultimately, any errors should lead the user to fall back to the approximation.